Distributed Trading Strategy Based on Value Recognition Mechanism for Power Sharing at Demand Side

doi:10.12204/j.issn.1000-7229.2022.08.014

Abstract

Abstract:

The integration of distributed generation, energy storage and flexible load endows the demand side with the ability of flexible regulation and control, enabling it to share electricity as a prosumer, in order to promote the optimal allocation of power resources. Therefore, this paper studies the transaction mechanism of power sharing market, and proposes a distributed trading strategy based on value recognition mechanism for power sharing at demand side, aiming to reduce the transaction cost of power market and improve the market efficiency. Firstly, the electricity sharing mode driven by marginal price is designed according to surplus theory, and the market game model is established on the basis of best reaction function, which reveals the meaningless loss caused by disorderly competition in the market. In this regard, a value recognition mechanism is proposed to improve the operation efficiency of power sharing market, and a distributed transaction strategy based on consistency algorithm is designed to realize decentralized transaction among prosumers, so as to protect the privacy of users. Finally, numerical simulation shows that the proposed trading strategy can achieve Pareto improvement of electricity sharing market and promote the optimal allocation of power resources.

Key words: electricity market, prosumer, power sharing, trading mechanism, consistency algorithm

CLC Number:

TM732

ZHAN Xiangpeng, YANG Jun, SHEN Yimin, QIAN Xiaorui, WANG Xinyan, WU Fuzhang. Distributed Trading Strategy Based on Value Recognition Mechanism for Power Sharing at Demand Side[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(8): 141-149.

Figures/Tables 11

Fig.1 The diagram of power sharing at demand side

Fig.2 Self-sufficiency of prosumer

Fig.3 The impact of scarcity on power sharing

Fig.4 The diagram of power sharing at demand side

Fig.5 The optimal response of prosumers A and B

Fig.6 The track of welfare in power sharing market

Fig.7 Power sharing network including 10 prosumers

Table 1 The preference of prosumers and the state of self-sufficient

Fig.8 The iterative process of consistent

Fig.9 The iterative process of power sharing

Table 2 The results of power sharing

References 28

[1]	罗筱均, 魏震波, 田轲, 等. 基于源荷匹配度的多综合能源社区日前共享模型[J]. 电力建设, 2021, 42(1): 20-27.
	LUO Xiaojun, WEI Zhenbo, TIAN Ke, et al. Day-ahead sharing model of multi-integrated energy community considering source-load matching degree[J]. Electric Power Construction, 2021, 42(1): 20-27.
[2]	HUANG W J, ZHANG N, KANG C Q, et al. From demand response to integrated demand response: Review and prospect of research and application[J]. Protection and Control of Modern Power Systems, 2019, 4: 12. doi: 10.1186/s41601-019-0126-4 URL
[4]	CHEN Y, WEI W, WANG H, et al. An energy sharing mechanism achieving the same flexibility as centralized dispatch[J]. IEEE Transactions on Smart Grid, 2021, 12(4): 3379-3389. doi: 10.1109/TSG.2021.3060380 URL
[3]	PARAG Y, SOVACOOL B K. Electricity market design for the prosumer era[J]. Nature Energy, 2016, 1: 16032. doi: 10.1038/nenergy.2016.32 URL
[5]	单俊嘉, 李阳, 胡俊杰. 基于交互能源机制的产消者能量管理方法[J]. 电力建设, 2019, 40(11): 31-38.
	SHAN Junjia, LI Yang, HU Junjie. Application of transactive energy mechanism in prosumer energy management[J]. Electric Power Construction, 2019, 40(11): 31-38.
[6]	杨昭, 艾欣. 考虑电能共享的综合能源楼宇群分布式优化调度[J]. 电网技术, 2020, 44(10): 3769-3778.
	YANG Zhao, AI Xin. Distributed optimal scheduling for integrated energy building clusters considering energy sharing[J]. Power System Technology, 2020, 44(10): 3769-3778.
[7]	吴界辰, 艾欣, 胡俊杰, 等. 面向智能园区多产消者能量管理的对等模型(P2P)建模与优化运行[J]. 电网技术, 2020, 44(1): 52-61.
	WU Jiechen, AI Xin, HU Junjie, et al. Peer-to-peer modeling and optimal operation for prosumer energy management in intelligent community[J]. Power System Technology, 2020, 44(1): 52-61.
[8]	寇凌峰, 张颖, 季宇, 等. 分布式储能的典型应用场景及运营模式分析[J]. 电力系统保护与控制, 2020, 48(4): 177-187.
	KOU Lingfeng, ZHANG Ying, JI Yu, et al. Typical application scenario and operation mode analysis of distributed energy storage[J]. Power System Protection and Control, 2020, 48(4): 177-187.
[9]	任文诗, 高红均, 刘友波, 等. 智能建筑群电能日前优化共享[J]. 电网技术, 2019, 43(7): 2568-2577.
	REN Wenshi, GAO Hongjun, LIU Youbo, et al. Optimal day-ahead electricity scheduling and sharing for smart building cluster[J]. Power System Technology, 2019, 43(7): 2568-2577.
[10]	HAN L, MORSTYN T, MCCULLOCH M. Incentivizing prosumer coalitions with energy management using cooperative game theory[J]. IEEE Transactions on Power Systems, 2018, 34(1):303-313. doi: 10.1109/TPWRS.2018.2858540 URL
[11]	ESSAYEH C, FENNI M R, DAHMOUNI H. Optimization of energy exchange in microgrid networks: A coalition formation approach[J]. Protection and Control of Modern Power Systems, 2019, 4(24): 1-10. doi: 10.1186/s41601-019-0115-7 URL
[12]	刘念, 王程, 雷金勇. 市场模式下光伏用户群的电能共享与需求响应模型[J]. 电力系统自动化, 2016, 40(16): 49-55, 131.
	LIU Nian, WANG Cheng, LEI Jinyong. Power energy sharing and demand response model for photovoltaic prosumer cluster under market environment[J]. Automation of Electric Power Systems, 2016, 40(16): 49-55, 131.
[13]	匡熠, 王秀丽, 王建学, 等. 基于stackelberg博弈的虚拟电厂能源共享机制[J]. 电网技术, 2020, 44(12): 4556-4564.
	KUANG Yi, WANG Xiuli, WANG Jianxue, et al. Virtual power plant energy sharing mechanism based on stackelberg game[J]. Power System Technology, 2020, 44(12): 4556-4564.
[14]	ANOH K, MAHARJAN S, IKPEHAI A, et al. Energy peer-to-peer trading in virtual microgrids in smart grids: A game-theoretic approach[J]. IEEE Transactions on Smart Grid, 2020, 11(2): 1264-1275. doi: 10.1109/TSG.2019.2934830 URL
[15]	CUI S C, WANG Y W, LIU N. Distributed game-based pricing strategy for energy sharing in microgrid with PV prosumers[J]. IET Renewable Power Generation, 2018, 12(3): 380-388. doi: 10.1049/iet-rpg.2017.0570 URL
[16]	CHEN Y, MEI S W, ZHOU F Y, et al. An energy sharing game with generalized demand bidding: Model and properties[J]. IEEE Transactions on Smart Grid, 2020, 11(3): 2055-2066. doi: 10.1109/TSG.2019.2946602 URL
[17]	王丹, 苏朋飞, 桂勋, 等. 售电侧市场开放环境下微网端对端电能交易关键技术综述及展望[J]. 电力建设, 2019, 40(1): 112-122.
	WANG Dan, SU Pengfei, GUI Xun, et al. Overview and prospect of key technologies of peer-to-peer energy trading in micro-grid under power-sales-side market liberalization[J]. Electric Power Construction, 2019, 40(1): 112-122.
[18]	MORSTYN T, TEYTELBOYM A, MCCULLOCH M D. Bilateral contract networks for peer-to-peer energy trading[J]. IEEE Transactions on Smart Grid, 2019, 10(2): 2026-2035. doi: 10.1109/TSG.2017.2786668 URL
[19]	MANKIW N G. 经济学原理-微观经济学分册[M]. 梁小民,梁砾,译. 北京: 北京大学出版社, 2015.
[20]	吴鸣, 赵婷婷, 赵凤展, 等. 微电网运行效果评价指标体系及评价方法[J]. 电网技术, 2018, 42(3): 690-697.
	WU Ming, ZHAO Tingting, ZHAO Fengzhan, et al. Evaluation index system of microgrid operation effect and corresponding evaluation method[J]. Power System Technology, 2018, 42(3): 690-697.
[21]	LE CADRE H, JACQUOT P, WAN C, et al. Peer-to-peer electricity market analysis: From variational to generalized nash equilibrium[J]. European Journal of Operational Research, 2020, 282(2): 753-771. doi: 10.1016/j.ejor.2019.09.035 URL
[22]	WEI W, LIU F, MEI S W. Energy pricing and dispatch for smart grid retailers under demand response and market price uncertainty[J]. IEEE Transactions on Smart Grid, 2015, 6(3): 1364-1374. doi: 10.1109/TSG.2014.2376522 URL
[23]	詹祥澎, 杨军, 韩思宁, 等. 考虑电动汽车可调度潜力的充电站两阶段市场投标策略[J]. 电力系统自动化, 2021, 45(10): 86-96.
	ZHAN Xiangpeng, YANG Jun, HAN Sining, et al. Two-stage market bidding strategy of charging station considering schedulable potential capacity of electric vehicle[J]. Automation of Electric Power Systems, 2021, 45(10): 86-96.
[24]	CHEN Y, ZHAO C H, LOW S H, et al. Approaching prosumer social optimum via energy sharing with proof of convergence[J]. IEEE Transactions on Smart Grid, 2021, 12(3): 2484-2495. doi: 10.1109/TSG.2020.3048402 URL
[25]	LIU Z X, WU Q W, OREN S S, et al. Distribution locational marginal pricing for optimal electric vehicle charging through chance constrained mixed-integer programming[J]. IEEE Transactions on Smart Grid, 2018, 9(2): 644-654. doi: 10.1109/TSG.2016.2559579 URL
[26]	阮博, 俞德华, 李斯吾. 基于一致性的微网分布式能量管理调度策略[J]. 电力系统保护与控制, 2018, 46(15): 23-28.
	RUAN Bo, YU Dehua, LI Siwu. Consensus algorithm based distributed energy management strategy of microgrids[J]. Power System Protection and Control, 2018, 46(15): 23-28.
[27]	张鑫, 邓莉荣, 李敬光, 等. 基于一致性算法的“源-网-荷-储”协同优化方法[J]. 电力建设, 2018, 39(8): 2-8.
	ZHANG Xin, DENG Lirong, LI Jingguang, et al. A “source-grid-load-storage”cooperative optimization method based on consensus algorithm[J]. Electric Power Construction, 2018, 39(8): 2-8.
[28]	张瑶, 王傲寒, 张宏. 中国智能电网发展综述[J]. 电力系统保护与控制, 2021, 49(5): 180-187.
	ZHANG Yao, WANG Aohan, ZHANG Hong. Overview of smart grid development in China[J]. Power System Protection and Control, 2021, 49(5): 180-187.